The choice of the ML model defines the type of function \(g\)  that we seek to learn. The function is often non-linear, since that gives the model additional degrees of freedom to fit the data. There are many available ML models, from deep ANNs [1]} to powerful implementations of regression decision trees and support vector machines (SVMs) [2]}. The hyperparmeters of the ML model have to also be carefully chosen [3]}. Those include parameters that strongly affect the model’s performance, although they are pre-fixed rather than
trained.
